Strategies to improve results from genomic analyzes in small dairy cattle populations

• Ano de defesa: 2019
• Autor(a) principal: Perez, Bruno da Costa
• Tipo de documento: Tese
• Tipo de acesso: Acesso aberto
• Idioma: eng
• Instituição de defesa: Biblioteca Digitais de Teses e Dissertações da USP
• Palavras-chave em Português: Associação genômica ampla; Community detection; Detecção de comunidade; Fenotipagem; Genome-wide association; Genotipagem; Genotyping; Phenotyping; Simulação; Simulation
• Link de acesso: http://www.teses.usp.br/teses/disponiveis/74/74131/tde-15082019-154456/

The main objective of the present thesis was to propose a procedure to optimize genotypic information value in small dairy cattle populations and investigate the impacts of including genotypes and phenotypes of cows chosen by different strategies over the performance of genome-wide association studies and genomic selection. The first study was designed to propose innovative methods that could support alternative inference over population structure in livestock populations using graph theory. It reviews general aspects of graphs and how each element relates to theoretical and practical concepts of traditional pedigree structure studies. This chapter also presents a computational application (PedWorks) built in Python 2.7 programming language. It demonstrates that graph theory is a suitable framework for modeling pedigree data. The second study was aimed asses how graph community detection algorithms could help unraveling population partition. This new concept was considered to develop a method for stablishing new cow genotyping strategies (community-based). Results obtained showed that accounting for population structure using community detection for choosing cows to get included in the reference population may improve results from genomic selection. Methods presented are easily applied to animal breeding programs. The third study aimed to observe the impacts of different genotyping strategies (including the proposed community-based) over the ability to detect quantitative trait loci in genome-wide association studies. Distinct models for genomic analysis were also tested. Results obtained showed that including cows with extreme phenotypic observations proportionally sampled from communities can improve the ability to detect quantitative trait loci in genomic evaluations. The last chapter was designed study possible deleterious impacts of the presence of preferential treatment (in different levels) in a small dairy cattle population environment over accuracy and bias of genomic selection. Different proportions of cows with artificially increased phenotypic observations were included in the reference population. Observed results suggest that both accuracy and bias are affected by the presence of preferential treatment of cows in the evaluated population. Preferential treatment is expected to have much more effect on the performance of genomic selection in small than in large dairy cattle populations for the higher (proportional) value of the information from cows in such reduced-size breeds.